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MIT researchers improve upon methods to model urban air pollution

Researchers at MIT have developed a new detailed air-quality model to simulate the effects of physical, chemical, and meteorological processing of highly reactive trace species in urban areas. The new metamodel is capable of efficiently simulating the urban concentration, surface deposition, and net export flux of these species that are important to human health and the global climate.

Urban regions account for an ever increasing fraction of Earth’s population, and are consequently an ever increasing source of air pollutants. These pollutants include anthropogenic aerosols, which have important climate and health implications. However, modeling aerosol emissions from urban areas is difficult due to the detailed temporal and spatial scales required. Thus, urban areas significantly contribute to the overall uncertainty and variability in global atmospheric model predictions of aerosol and pollutant distribution.

To address these uncertainties, researchers from the MIT Joint Program on the Science and Policy of Global Change set out to see if they could better model aerosol emissions and distribution from urban regions. To accurately model urban areas, factors such as the amount and distribution of emissions, the meteorological and geographical properties of the region, and the chemical and physical processing of emissions over time would need to be considered on spatial and temporal scales much smaller than global models. Previously, modelers have attempted to account for urban aerosol emissions by using a correction factor, which diluted total aerosol emissions across global model grid cells. This dilution method, however, does not capture the heterogeneity of urban and non-urban areas within each grid cell.

A polynomial chaos expansion and the probabilistic collocation method have been used to develop the metamodel, and its coefficients, so that it is applicable under a broad range of present-day and future conditions. The inputs upon which this metamodel have been formed are based on a combination of physical properties (average temperature, diurnal temperature range, date, and latitude), anthropogenic properties (patterns and amounts of emissions), and the nature of the surrounding environment (background concentrations of species).

The metamodel development involved using probability distribution functions (PDFs) of the inputs to run a detailed parent chemical and physical model, the Comprehensive Air Quality Model with Extensions (CAMx), thousands of times. Outputs from these runs were used in turn to both determine the coefficients of and test the precision of the metamodel, as compared with the detailed parent model.

It was determined that the deviations between the metamodel and the parent mode for many important species (O3, CO, NOx, and black carbon (BC)) were found to have a weighted RMS [root mean square] error less than 10% in all cases, with many of the specific cases having a weighted RMS error less than 1%. Some of the other important species (VOCs, PAN, OC, and sulfate aerosol) usually have their weighted RMS error less than 10% as well, except for a small number of cases. In these cases, the complexity and non-linearity of the physical, chemical, and meteorological processing is too large for the third order metamodel to give an accurate fit.

...sensitivity tests have been performed, to observe the response of the 16 metamodels (4 different meteorologies and 4 different urban types) to a broad set of potential inputs. These results were compared with observations of ozone, CO, formaldehyde, BC, and PM10 from a few well observed urban areas, and in most of the cases, the output distributions were found to be within ranges of the observations.

—Cohen and Prinn

Cohen and Prinn used meteorological and emissions data from 16 representative urban areas. The urban processing model examined seven different types of aerosols of different sizes and composition, and modeled a total of 251 urban areas, including 91 from China, 36 from India, 50 from developed nations (Australia, Canada, EU, Japan, Singapore, South Korea, US) and 74 from developing nations. The urban processing model was then included into a larger global model that simulates atmospheric chemistry and transport at regional to global scales.

Not only are we the first group to successfully incorporate an urban-scale chemical processing model into a 3-dimensional global model, but our results resolve important processes which the rest of the modeling community still neglects to include.

—Dr. Jason Cohen, lead author

The study found that the urban processing model predicted a lower concentration of atmospheric aerosols than the dilution method, particularly in the Northern Hemisphere and in the summer season. In addition, the urban processing model showed increased concentrations of primary aerosols, such as black carbon and organic carbon, and decreased concentrations of secondary aerosols, like sulfates. Thus excluding the urban processing model could lead to an overestimation of some aerosols and an underestimation of others.

The reason these biases exist in the dilution method is that urban areas tend to be more efficient at oxidizing and removing substances like black carbon and organic carbon from the atmosphere—not taking this into consideration leads to an overestimation of the concentration of these species. Because these aerosol species are oxidized, generation of the secondary aerosol species actually increase in urban areas—not taking this into consideration leads to an underestimation of the concentration of those species.

Aerosols tend to cause negative radiative forcing. In other words, they have an overall “cooling effect” on the global climate. But using the urban processing method instead of the dilution method demonstrated an overall smaller concentration of aerosols in the atmosphere. Thus the detailed urban processing model predicts significantly less negative aerosol radiative forcing (less cooling) than the dilution method.

We are continuing this effort, looking at the long-term climate effects of using detailed urban processing, such as how average surface temperature, precipitation, and cloud cover will be impacted. We hope that as we continue to look into the impacts of this new methodology and continue to learn more about the mistakes that the dilution simplification have led to, that others in the climate modeling community will adopt and use our new standard.

—Jason Cohen




"...modeling aerosol emissions from urban areas is difficult due to the detailed temporal and spatial scales required. Thus, urban areas significantly contribute to the overall uncertainty and variability in global atmospheric model predictions of aerosol and pollutant distribution.

Given what we are learning about massive variable inputs to atmospheric science - these sims will struggle to be effective for a long while.

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